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In this work we propose an approach for estimating 3D human poses of multiplepeople from a set of calibrated cameras. Estimating 3D human poses frommultiple views has several compelling properties: human poses are estimatedwithin a global coordinate space and multiple cameras provide an extended fieldof view which helps in resolving ambiguities, occlusions and motion blur. Ourapproach builds upon a real-time 2D multi-person pose estimation system andgreedily solves the association problem between multiple views. We utilizebipartite matching to track multiple people over multiple frames. This proofsto be especially efficient as problems associated with greedy matching such asocclusion can be easily resolved in 3D. Our approach achieves state-of-the-artresults on popular benchmarks and may serve as a baseline for future work.

Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views